ORIGINAL ARTICLE

Neuroanatomic Correlates of PsychopathologicComponents of Major Depressive DisorderMatthew S. Milak, MD; Ramin V. Parsey, MD, PhD; John Keilp, PhD; Maria A. Oquendo, MD;Kevin M. Malone, MD; J. John Mann, MD

Background: The Hamilton Depression Rating Scale(HDRS) is widely used to measure the severity of depres-sion in mood disorders. Total HDRS score correlates withbrain metabolism as measured by fludeoxyglucose F 18([18F]-FDG) positron emission tomography. The HDRScomprises distinct symptom clusters that may be asso-ciated with different patterns of regional brain glucosemetabolism.

Objective: To examine associations between HDRS com-ponent psychopathologic clusters and resting glucose ce-rebral metabolism assessed by [18F]-FDG positron emis-sion tomography.

Patients: We evaluated 298 drug-free patients who metthe DSM-III-R criteria for major depressive disorder.

Main Outcome Measures: Five principal compo-nents were extracted from the 24-item HDRS for all sub-jects and ProMax rotated: psychic depression, loss of mo-tivated behavior, psychosis, anxiety, and sleep disturbance.The [18F]-FDG scans were acquired in a subgroup of 43drug-free patients in twelve 5-minute frames. Voxel-

level correlation maps were generated with HDRS totaland factor scores.

Results: Total HDRS score correlated positively with ac-tivity in a large bilateral ventral cortical and subcorticalregion that included limbic, thalamic, and basal gangliastructures. Distinct correlation patterns were found withthe 3 individual HDRS factors. Psychic depression cor-related positively with metabolism in the cingulate gy-rus, thalamus, and basal ganglia. Sleep disturbance cor-related positively with metabolism in limbic structuresand basal ganglia. Loss of motivated behavior was nega-tively associated with parietal and superior frontal cor-tical areas.

Conclusions: Different brain regions correlate with dis-crete symptom components that compose the overall syn-drome of major depression. Future studies should ex-tend knowledge about specific regional networks byidentifying responsible neurotransmitters related to spe-cific psychopathologic components of mood disorders.

Arch Gen Psychiatry. 2005;62:397-408

M AJOR DEPRESSIVE EPI-sodes (MDEs) involveseveral psychopatho-logic components.Brain imaging stud-

ies1-28 have identified abnormalities asso-ciated with MDEs, but most have not at-tempted to identify brain regions relatedto symptom components of MDEs. Symp-tom components may correlate with dif-ferent brain regions. If so, given the varia-tion in psychopathologic features and theirseverity between episodes, even within thesame individual,29 considering only globaldepression severity in mapping brain ac-tivity patterns in MDEs introduces noiseinto the results of studies.

One approach to obtaining a map of theanatomic correlates of the symptom com-ponents of MDEs is to decompose the over-all depression rating scale scores into com-ponents. The internal structure of clinicalpsychometric scales can be explored by fac-

tor analysis. Despite progress in resolvingthe technical30 and conceptual31-35 issues,previous factor analyses of the Hamilton De-pression Rating Scale (HDRS)36-54 identi-fied 1 to 7 factors. The lack of consistentresults reflects methodologic and studypopulation differences.30,37,48 For ordered-category ratings, the polychoric correla-tion technique (see the “Methods” sec-tion) is an option55-61 that avoids potentialartifacts and yet, to the best of our knowl-edge, has never been applied to determinethe correlation between HDRS factors andrelative regional brain activity as mea-sured by glucose uptake (rCMRglu).

To our knowledge, only 3 studies62-64

have evaluated the correlation between theseverity of symptom clusters obtained byfactor analysis and rCMRglu63,64 or rela-tive regional cerebral blood flow (rCBF).62

All these studies found indications of re-gional differences in patterns of correla-tion with symptom components, al-

Author Affiliations:Departments of Psychiatry andRadiology, Columbia University,and Department ofNeuroscience, New York StatePsychiatric Institute, New York.

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though 1 study63 examined only treatment effects. Thesestudies used small, diagnostically heterogeneous samples,limiting the confidence in the factors derived.

To overcome the limitations of the previous studies,we conducted factor analysis of depressive symptom clus-ters in 298 medication-free patients with a current DSM-III-R MDE using a polychoric correlation matrix of 24-item HDRS (HDRS-24)42,65 scores that generated 5nonorthogonal symptom factors. We then examined therelationships between these factors and the HDRS-24 totalscore at a voxel level to rCMRglu measured by fludeoxy-glucose F 18 ([18F]-FDG) and positron emission tomog-raphy (PET) in 43 of the 298 patients.

METHODS

PATIENTS

Medication-free patients with a current MDE in the context ofmajor depressive disorder, diagnosed based on the StructuredClinical Interview for DSM-III-R, Patient Version,66 and with ascore greater than 16 on the 17-item HDRS41,42,65 were enteredinto the study after giving written informed consent as ap-proved by the Columbia University and New York State Psy-chiatric Institute institutional review boards. Demographic dataand psychiatric, medical, and family histories were recordedon the Columbia Baseline Demographic Form. Patients wereadministered the HDRS within 24 hours of undergoing PET.

Data are reported as mean±SD. Patients in the PET analysishad an age of 38.4±13.2 years and 15.6±2.9 years of education.Age at the first episode of major depression was 23.8±14.7 years.The cohort comprised 61% women and had 4.2±3.5 lifetime epi-sodes of major depression. The Global Assessment of Function-ing Scale score for the current episode was 43.6±10.2. TheHDRS-24 total and factor scores for patients in the PET analysiswere as follows: HDRS-24 total score, 29.7±6.2 (scale range, 0-74);factor 1, 12.5±3.3 (scale range, 0-28); factor 2, 5.1±2.3 (scalerange, 0-10); factor 3, 1.6±1.7 (scale range, 0-12); factor 4, 5.3±2.1(scale range, 0-14); and factor 5, 2.8±1.7 (scale range, 0-6).

Patients were medication free for a minimum of 14 days ex-cept for benzodiazepines and 1 patient receiving buspirone hy-drochloride (6 weeks in the case of fluoxetine hydrochlorideand 1 month in the case of oral antipsychotic agents). The me-dian number of days not taking each type of medication be-fore PET was as follows: anticonvulsants and mood stabiliz-ers1 (n=5), 19 (range, 13-34 days); antidepressant, other2 (n=7),43 (range, 7-13 days); benzodiazepines (n=17), 25 (range, 9-956days); selective serotonin reuptake inhibitor, non-fluoxetine3

(n=8), 26.5 (range, 9-461 days); and fluoxetine (n=7), 64,(range, 41-984 days). Eighteen patients had no previous medi-cation use. The following medications were taken by 1 patienteach: lithium carbonate (terminated 43 days before PET), themonoamine oxidase inhibitor phenelzine sulfate (41 days), andlevothyroxine sodium (16 days). The following medications weretaken by 2 patients each: the antiparkinsonian drugs bromo-criptine and pergolide mesylate (terminated 101 and 29 daysbefore PET), risperdone (41 and 29 days), the typical antipsy-chotic agents haloperidol and thioridazine (44 and 30 days),the stimulants dextroamphetamine sulfate and methylpheni-date hydrochloride (19 and 16 days), and electroconvulsivetherapy (56 and 57 days). Three patients each took the follow-ing medications: buspirone hydrochloride (terminated 9, 17,and 44 days before PET), the tricyclic antidepressants clomip-ramine hydrochloride (n=2) and nortriptyline hydrochloride(n=1) (19, 82, and 956 days),1 divalproex sodium and carba-mazepine,2 trazodone hydrochloride, venlafaxine hydrochlo-

ride, mirtazapine, nefazodone hydrochloride, bupropion hy-drochloride,3 paroxetine, and sertraline hydrochloride.

Patients were free of medical illnesses based on history, physi-cal examination findings, and laboratory test results. Pregnantwomen were excluded. Premenopausal women were studiedwithin 5 days of the onset of menses.

FACTOR ANALYSIS

The factor analysis was performed on the polychoric correla-tion matrix of the HDRS-24 scores. For completeness of con-tent coverage of components of MDEs, we used HDRS-24 scoresfor the factor analysis. The polychoric correlation (for ordered-category ratings) is preferable to correlation or covariance ma-trices for the measurement of correlations between psychomet-ric scale items55-59 because it is theoretically invariant acrosschanges in the number or “width” of rating categories. Other-wise, owing to the truncated range of scores (inclusion criteriaof the 17-item HDRS total score of �17) and the stepwise na-ture of the subitem scores (each item ordered into a few catego-ries and scored from zero to a single digit upper limit, which var-ies from item to item), the standard factor analysis of a correlationmatrix or covariance matrix can generate false associations be-tween items. We previously used this method in an analysis ofthe Beck Suicide Intent Scale.67 However, we also ran our factoranalysis using standard Pearson correlations and the raw data,and we obtained the same 5 factors that we obtained with thepolychoric matrix (all items loaded on the same factors). Be-cause we did not weight factor scores by their loadings, the Pear-son correlation matrix produced identical factor scores. Corre-lations with 18F-FDG uptake remained unchanged.

Furthermore, we used a nonorthogonal (ProMax) rota-tion. Applying a mathematical rotation to the axes can greatlysimplify the relationships between factors (axes) and vari-ables (HDRS item scores). A multidimensional factor plot mayhave multiple distinct clusters, which are isolated from eachother but vectorially less than orthogonal to each other. In sucha case, orthogonal rotation of the axes would not necessarilystop variables from loading equally on several axes or factors.A nonorthogonal rotation of the axes is necessary to find a use-ful factor solution to variables that tend to form clusters thatare not orthogonal to each other.

The same factor structure of depressive symptoms was foundin the subsample that underwent PET (n=43) and the largersample (n=298). The 2 groups also did not differ in HDRS totalor factor scoresordemographicvariables except that thePETgroupwas more educated by a mean of 1.5 years (t60.76=–3.13; P=.003).

PET STUDIES

As reported in previous publications,12,68-70 a bolus injection ofapproximately 10 mCi of 18F-FDG was administered intrave-nously. Patients gazed at crosshairs in a room with dimmed light-ing during the first 15 minutes of the 18F-FDG distribution phaseand then rested quietly for another 15 minutes before movingto the scanner (ECAT EXACT 47; Siemens Corp, New York, NY),where they were supine for 10 minutes before undergoing PET.

IMAGE ANALYSIS

As reported elsewhere,12,68-70 the twelve 5-minute PET frameswere aligned using automated image registration71 and thensummed. Statistical analysis was performed using Statistical Para-metric Mapping (SPM99; Institute of Neurology, University Col-lege of London, London, England) implemented in Matlab 5(The Mathworks Inc, Natick, Mass).72,73 To determine whichregions correlate with HDRS-24 total and factor scores, a voxel-level correlation analysis was performed using the general lin-

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ear model with rCMRglu. Height threshold was set a priori toP� .01, and the extent threshold was set to P� .05 after cor-rection for multiple comparisons by Statistical Parametric Map-ping. Stereotaxic coordinates reported are based on Talairachatlas74 coordinates, converted from Montreal Neurological In-stitute coordinates.75-77

RESULTS

FACTOR ANALYSIS

Factor analysis of the HDRS-24 on the population of 298depressed patients yielded a 5-factor solution (Table 1).When factors are correlated owing to nonorthogonal ro-tation, sums of square loadings cannot be added to ob-tain a total cumulative variance. However, from the eig-envalues, it can be determined that the variance inHDRS-24 scores explained by the individual factors rangesfrom 9.4% to 13.1%. For descriptive statistics on the sub-scale (or factor) scores, see the “Methods” section.

rCMRglu CORRELATION WITH HDRS SCORES

HDRS-24 Total Score

There are positive correlations between rCMRglu and theHDRS-24 total score in multiple ventral brain regions(Figure 1 and Table 2). These structures form a singlecontiguous brain region (6960 voxels) in which rCMRglushows significant (cluster level P� .001, corrected for mul-tiple comparisons) positive correlation (partial R=0.551;global maximum at Talairach coordinates 10 13 –12) withHDRS-24 total scores. These brain regions involve thebilateral mesiotemporal cortex, parts of the ventral sub-genual basal forebrain, and most of the thalamus, hypo-thalamus, ventral striatum, and midbrain. The HDRS-24total score shows no significant negative correlation withany brain region.

Factor 1: Psychic Depression

Factor 1 correlates positively with a large central, ven-tral cortical and subcortical area that extends into the lefttemporal lobe (Figure 1, Figure 2, and Table 2). Thesestructures form 3 clusters (3557, 3793, and 1561 vox-els) in which rCMRglu shows significant (P� .001, .001,and .03) positive correlations (partial R=0.6, 0.5, and 0.6for maxima at Talairach coordinates −38 –45 –11, 10 –178, and –12 –23 40) with factor 1. This area includes mostof the dorsal posterior cingulate, thalamus, ventral stria-tum, hypothalamus, subgenual anterior cingulate, andsubgenual basal forebrain. Factor 1 shows no signifi-cant negative correlation with rCMRglu.

Factor 2: Loss of Motivated Behavior

Factor 2, in contrast to the total HDRS-24 and factor 1,shows only a significant negative correlation with largelydorsal cortical regions. These structures form 3 clusters(2873, 3765, and 3172 voxels) in which rCMRglu showsa significant (P� .001) negative correlation (partial R=0.6,0.6, and 0.5 at 42 –70 33, –22 –70 33, and –30 23 34

Talairach coordinates) with factor 2. Factor 2 is nega-tively correlated with an extensive network of dorsal cor-tical regions (Figures 1 and 2 and Table 2), including thedorsolateral prefrontal cortex (PFC), dorsal parietal cor-tex, and dorsal temporal association cortices.

Factors 3 and 4: Psychosis and Anxiety

Factors 3 and 4 show no significant positive or negativecorrelation with rCMRglu in any brain regions.

Factor 5: Sleep Disturbance

Factor 5 correlates positively with rCMRglu in a series ofregions almost encircling the area associated with factor1 (Figures 1 and 2 and Table 2). These structures form 2clusters (1582 and 5224 voxels) in which rCMRglu showsa significant (P�.003) positive correlation (partial R=0.6and 0.5 at 42 –20 32 and –24 –25 14 Talairach coordi-nates) with factor 5. The sleep disturbance factor showsno significant negative correlation with any brain region.

We found no correlations between HDRS total or fac-tor scores and age, sex, or any other demographic vari-ables reported in the “Methods” section. Nevertheless,for completeness we repeated the entire analysis con-trolling for age and sex. Despite the decreased statisticalpower, and correcting for multiple comparisons, the cor-relations between rCMRglu and the depression severity

Table 1. Factor Structure of the Hamilton Depression RatingScale (HDRS)

24-Item HDRS Factor Loading*

Factor 1: Psychic depression1. Depressed mood 0.592. Feelings of guilt 0.463. Suicide 0.678. Retardation 0.44

22. Helplessness 0.4123. Hopelessness 0.6524. Worthlessness 0.79

Factor 2: Loss of motivated behavior7. Work and activities 0.42

12. Somatic symptoms (appetite) 0.8414. Genital symptoms (libido) 0.5016. Weight loss 0.74

Factor 3: Psychosis17. Insight 0.7419. Depersonalization and derealization 0.4120. Paranoid symptoms 0.6821. Obsessive and compulsive 0.68

Factor 4: Anxiety9. Agitation 0.74

10. Anxiety—psychic 0.6211. Anxiety—somatic 0.5215. Hypochondrias 0.68

Factor 5: Sleep disturbance4. Insomnia—early 0.745. Insomnia—middle 0.836. Insomnia—late 0.59

*Each number represents the correlation between the item and the rotatedfactor. The extraction method used was principal components analysis, andthe rotation method used was ProMax with Kaiser normalization. Only itemswith factor loading of 0.5 or higher were retained for further analysis.

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scores remained statistically significant in all but 1 case.Factor 2 lost significance when including age in the sta-tistical design.

COMMENT

To our knowledge, this is the first study that maps theneuroanatomic correlates of the symptom componentsof major depression based on factor analysis of the poly-choric correlation matrix (instead of the correlation ma-trix or covariance matrix that is used routinely) of theHDRS-24. We found distinct correlation maps of brainactivity for 3 of the 5 factors. There is minimal overlapin the parametric maps of the 3 factors such that theirbrain distributions are strikingly distinct. This is best il-

lustrated in Figure 2, where the panel shows the areaunique to the corresponding factor separately from thearea that overlaps with other factors. Two factors did notreveal any statistically significant correlations with spe-cific brain regions in this population.

OVERALL DEPRESSION SEVERITY ANDRELATIVE REGIONAL BRAIN ACTIVITY

Overall depression severity shows a positive correlationwith rCMRglu in a large contiguous volume that in-cludes parts of the limbic system, the ventromedial pre-frontal and temporal cortices, parts of the inferior pari-etal cortex, the thalamus, the ventral aspects of the basalganglia, and the midbrain. No negative correlation of over-

Figure 1. Regions shown as a volume in the glass brain. Maps of correlations of relative regional glucose metabolic rate in human brain in major depression, withseverity of depression measured by the 24-item Hamilton Depression Rating Scale. Upper left, overall depression (total score). Upper right, Factor 1: psychicdepression. Lower left, Factor 2: loss of motivated behavior. Lower right, Factor 5: sleep disturbance.

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all depression score with rCMRglu was found in any brainregion. Like our findings, others have found overall se-verity of depression to be positively correlated withrCMRglu or rCBF in the ventral brain regions, such asthe bilateral medial frontal and right anterior cingu-late,78 right dorsolateral78 and anterolateral79 PFCs andin the hippocampus,80 cingulate, and other paralimbicareas.81 In addition, positive correlations are reported inthe left anterior temporal, left dorsolateral prefrontal, rightprefrontal, and right posterior temporal cortices.82 Somestudies report no correlation between overall severity ofdepression and central nervous system activity esti-mated by either rCBF83,84 or rCMRglu.85,86

There is less clear agreement among studies that findnegative correlations between global depression sever-ity and regional brain activity in terms of the specific re-gions involved. For example, overall severity of depres-sion is reported to be negatively correlated with rCMRgluor rCBF in whole slice,87 right cingulate cortex, bilateralPFC, insula, basal ganglia, and temporoparietal cortex(right� left)14; globally,88,89 inferior anterior cingulate cor-tex,90 anterofrontal and left prefrontal regions91; wide-spread anterior,92 frontal, central, superior temporal, andanterior parietal regions21; ventral anterior cingulate; andorbitofrontal cortex with the caudate nuclei in an acutetryptophan depletion–induced relapse paradigm.93 Simi-larly, low gray matter rCBF was reported to correlate withseverity of depression as measured by the HDRS.89 Someregions showing correlation with overall severity scoresin that study89 were identified in our study as correlat-

ing with specific clinical components of the HDRS. Oneexplanation for the inconsistency in reported findings isthat the diversity of clinical manifestations of MDE be-tween and within patients29 may obscure associations ofglobal severity with specific brain regions in functionalimaging studies. Psychopathologic item clusters corre-late differently with activity of specific brain regions suchthat combining factor scores from different subjects mayobscure important associations between brain regions andsymptom severity of depression.

Normalization of higher rCMRglu in the limbic sys-tem associated with improvement in MDEs63 after treat-ment, and increases in limbic-paralimbic rCBF (sub-genual cingulate and anterior insula) and decreases inneocortical rCBF in other regions (right dorsolateral pre-frontal and inferior parietal)94 indicate partial normaliza-tion with recovery from depression, namely, limbic meta-bolic decreases and neocortical metabolism increases. Asignificant inverse correlation between subgenual cingu-late and right dorsolateral prefrontal activity is demon-strated in the state of induced sadness and recovery.94

Caution should be exercised in interpreting negativefindings in this study. The fact that this study did notfind regions in the brain that negatively correlate withoverall depression does not mean that such regions donot exist. The composition of the group in terms of se-verity of factor 2, which has negative correlations withlarge bilateral parts of the PFC and parietotemporal cor-tex, may be part of the reason for the absence of a globalseverity negative correlation.

Table 2. Regions in Which Relative Cerebral Glucose Metabolism Shows Significant Correlations With Hamilton Depression RatingScale (HDRS) Total and Factor Scores*

HDRS Total† Factor 1† Factor 2‡ Factor 5†

Right mesiotemporal 28, 35, 36 . . . . . . . . .Left mesiotemporal 37, 19, 28, 35, 36 35, 36 . . . 19, 28, 30, 34, 35, 36, 37, amygdala,

hippocampusVentromedial prefrontal cortex BL 25 BL 25 . . . R 11, R 13, BL 25, R 47Dorsomedial prefrontal cortex . . . . . . BL 6, 8, 9 . . .Dorsolateral prefrontal cortex . . . . . . L 44, 45 . . .Ventral anterior cingulate BL 25 BL 25 . . . L 25, L 32Dorsal anterior cingulate . . . BL 24 . . . . . .Pregenual cingulate . . . . . . . . . . . .Dorsal posterior cingulate . . . BL 31 . . . . . .Insula BL 13 BL 13 . . . BL 13Left parietal cortex . . . . . . 7, 19, 39, 40 . . .Right parietal cortex . . . . . . 19, 39, 40 . . .Left temporal cortex 20, 37 20, 21, 22, 37 39 . . .Right temporal cortex . . . . . . 19, 22, 39 . . .Left occipital cortex . . . . . . 19, 39 . . .Right occipital cortex . . . . . . 19 . . .Thalamus BL mammillary body, BL MDN,

BL pulvinar, BL VAN, BL VLNEntire (BL) . . . . . .

Hypothalamus BL BL . . . . . .Caudate . . . BL head . . . R tailPutamen . . . . . . . . . BLGlobus pallidus . . . . . . . . . BLMidbrain BL most . . . . . . . . .Cerebellum . . . . . . . . . BL anterior lobe

Abbreviations: BL, bilateral; L, left; MDN, medial dorsal nucleus; R, right; VAN, ventral anterior nucleus; VLN, ventral lateral nucleus; ellipses, not applicable.*Numbers represent Brodmann areas.†Positive correlation.‡Negative correlation.

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5

4

3

2

1

0Figure 2. A map of correlations of relative regional glucose metabolic rate in human brain in major depression, with severity of depression measured by factors1, 2, and 5 of the 24-item Hamilton Depression Rating Scale. The color scales indicate the strength (t score) of the correlation (t score maps are overlaid on aseries of transaxial slices [2 mm apart] of a coregistered magnetic resonance image from 38 mm below to 72 mm above the line connecting the anterior andposterior commissures). Red to orange regions are uniquely positively correlated with factor 1, green to light green regions correlate with factors 1 and 5, blueto light blue regions are uniquely negatively correlated with factor 2, and red to light red regions are uniquely positively correlated with factor 5, the sleepdisturbance factor.

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CORRELATIONS WITH DEPRESSIVEPSYCHOPATHOLOGIC COMPONENTS

Factor 1: Psychic Depression

This factor, which includes items that reflect depressedmood, depressive cognitions, and suicidality, correlatespositively with a large ventral and midline area. Subjec-tive severity of negative cognitions in major depressionis reported by other researchers64 to also correlate withmetabolism bilaterally in ventral brain regions. Minor dif-ferences in involved brain regions compared with ourstudy may be attributable to differences in the clinicalmeasures used. Although the HDRS-derived factors in atreatment study63 differ somewhat from ours, there is con-vergence in the results because improvement in symp-tom severity was associated with a decrease in meta-bolic activity in ventral structures, a normalization of thefindings we made in the depressed state.

Factor 2: Loss of Motivated Behavior

Factor 2 is negatively correlated with an extensive net-work of dorsal cortical regions (Figures 1 and 2 andTable 2). Consistent with this finding, psychomotorchange–anhedonia is reported to correlate robustly withlower normalized rCMRglu in the right dorsolateralprefrontal and temporal cortices,64 and lower normal-ized rCBF in the dorsolateral anteroposterior PFC cor-relates with psychomotor slowing, poverty of speech,and cognitive impairment.62,95-98 On the other hand,psychomotor retardation–anhedonia correlated withlower absolute metabolism in the right insula, claus-trum, anteroventral caudate/putamen, and temporalcortex and with higher normalized metabolism in ante-rior cingulate in another study.64 Consistent with ourresults, rCMRglu in the left anterolateral and dorsolat-eral PFC increases proportionately with antidepressanttreatment response.63,79

We found that loss of motivation in depression wasalso associated with changes in the parietal, temporal, andfrontal cortices. Some studies99-103 have noted associa-tions in planning or other measures of motivation to pa-rietal and frontal cortex activity. In addition, one model104

has postulated that depression involves the frontal, lim-bic, and subcortical regions, with the subcortical re-gions playing a primarily gating role. Our findings thatmultiple regions are involved in this dimension of de-pression are in keeping with such a model and are fur-ther supported by studies that suggest specific deficitsin the parietal and frontal cortices that are associated withmotivation or its inverse, apathy.102

Although in animal105 and human106,107 studies, reward-related motivation has been linked to the striatum andto limbic projections from the midbrain tegmentum inparadigms using tasks in which some operantly condi-tioned behavior is coupled with the anticipation of in-stant gratification, it is not surprising that the metabolicabnormality of an extensive network of frontal, parietal,and temporal association cortices correlates with the se-verity of the loss of motivated behavior. This is in keep-ing with the model that dopaminergic input to the stria-

tum gates the glutamatergic sensorimotor and incentivemotivational input signals to the striatum,105 and it is alsosupported by studies of brain injuries in which reducedgoal-directed behavior due to lack of motivation (apa-thy) has been found to be associated with specific cog-nitive deficits related to frontal cortical dysfunction.102

Factor 3: Psychosis

Factor 3 shows no significant associations with rCMRgluin the present study. These items are the least cohesivegroup of items, forming a cluster in our factor analysisthat seems to encompass a dimension that is related topsychosis. Distinct patterns of central nervous system cor-relates of various measures of formal thought disorderand other psychotic symptoms are extensively re-viewed108 in the context of schizophrenia. The severityof psychosis was low in our sample, providing minimalstatistical power to find meaningful correlations.

Factor 4: Anxiety

We found no correlations with this factor, in contrast toother voxel-based correlational analyses that derived anxi-ety factors from the Beck Depression Inventory or otherscales.62,63 However, most region of interest–based stud-ies do not agree on correlations with anxiety severity. Posi-tive correlations are reported with 1 brainstem region ofinterest between rCBF and subjective anxiety scores.109 A“probable association” was reported81 between an in-crease in the anxious-depression factor and reduced fron-tal neocortical perfusion. No clear association was re-ported110 between subjective or physiologic variables andchanges in rCBF as a consequence of anxiety induction.Based on an anxiety induction study, it has been sug-gested111 “that some of the temporal cortex rCBF activa-tion peaks previously reported in humans in associationwith drug- and non–drug-induced anxiety, as well as theincrease in rCBF in the claustrum-insular-amygdala re-gion, may be of vascular and/or muscular origin” insteadof a reflection of central nervous system activity. On theother hand, there are rCMRglu112 and rCBF113 studies thatshow disparate but unique patterns of correlation be-tween their measures of anxiety and tracer uptake. An-other possible explanation for the disparate findings, be-sides differences in patient populations and definitions ofanxiety symptoms, may lie in the uncoupling of metabo-lism and blood flow in many brain regions.114 This double-isotope 18F-FDG and technetium Tc 99m–hexamethyl-propylene amine oxime single-photon emission computedtomography technique indicates that a dynamic couplingbetween rCBF and rCMRglu exists only in a few distinctbrain regions even in healthy individuals, and depressiveillness may have a further uncoupling effect on this cor-relation in some brain regions.

Factor 5: Sleep Disturbance

Factor 5 shows positive correlations with a series of cor-tical and subcortical structures in our awake patients. Ab-normalities in brain activity in the limbic and paralimbicstructures that overlap with regions we find to correlate

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with the depression sleep disturbance factor are reportedto be more active in relation to sleep disturbances foundin major depression11 and to decreases in activity after sleepdeprivation treatment of major depression.115

These results are also consistent with findings fromhuman functional neuroimaging studies of sleep (for areview see Maquet116). It is currently assumed that forthe successful initiation or maintenance of physiologicphases of normal sleep, the deactivation of these areas isimportant. Consequently, it is not surprising that insom-nia (a lack of normal sleep) may positively correlate withthe degree to which some of these areas are overacti-vated and, therefore, perhaps fail to deactivate and therebyinterfere with the development of normal sleep archi-tecture. In contrast, a dissenting study63 found that im-provement in sleep disturbance was negatively associ-ated with change in right anterior medial temporal, leftventral frontal, and right ventral frontal metabolism.

IMPLICATIONS FOR THE NEURAL CIRCUITRYOF MAJOR DEPRESSION

The evolution of emotion likely stems from the absoluteneed to identify and appraise threatening and reward-ing stimuli in the environment and form quick and ap-propriate goal-directed behavior in response.117,118 Thisprocess has been divided into identification and ap-praisal of the emotional significance of the stimulus andthe production of an affective state, including the auto-matic physiologic and somatomotor responses to thestimuli.119-121 Phillips et al122 propose a third process, theeffortful regulation of the affective state and behavioralresponses, which in turn involves the inhibition or modu-lation of the first 2 processes so that the affective stateand behavior produced are contextually appropriate. Theneural basis of these 3 processes have been extensivelyreviewed recently.122,123 Briefly, amygdala, which has ex-tensive interconnections with insula, together with theventromedial PFC, thalamus, hypothalamus, and peri-aqueductal gray, is thought to form part of a network thatparticipates in perceiving aversive stimuli and organiz-ing autonomic responses to them. Numerous lines ofevidence,124,125 human stimulation,126 human central ner-vous system lesions,127-131 and functional brain imagingstudies132-143 support the involvement of these structuresin the perception of aversive stimuli124,125 or the identifi-cation of expressions of fear,128-130,144 disgust,76,131,136,145,146

sadness,139 and happiness134 and in the attention147 toemotionally charged information.

Findings from animal and human studies suggest thatthese structures participate not only in the perception andidentification of the emotional salience of stimuli but alsoin the second process mentioned in the previous para-graph, that is, the production of affective states and emo-tional behavior. Animal studies implicate the ventral teg-mental area, nucleus accumbens,148,149 putamen, andcaudate125 in reward processing; the amygdala150-155 in theproduction of various affective states; the subgenualanterior cingulate156-160 in autonomic and conditionedresponses to emotionally salient stimuli; and the sub-genual ventral PFC161-163 in the evaluation of the rewardvalue of stimuli and the regulation of autonomic and

endocrine responses to fear.164 Human lesion165-168 andstimulation169,170 studies support the involvement of thesestructures (the amygdala,165,166,169 subgenual anterior cin-gulate,167,168,170 and ventromedial PFC171-174) in the pro-duction of affective states and emotional behavior in hu-man. Findings from human functional brain imagingstudies support the involvement of these structures (foundin the present study to correlate with depression sever-ity) in the production of affective states and emotionalbehavior (the ventral striatum,175-179 amygdala,180-184 sub-genual anterior cingulate,94,185,186 orbitofrontal/ventro-medial PFC,186-193 and dorsal anterior cingulate194-198). Partsof the dorsolateral PFC, found to have a negative corre-lation with factor 2 in our study (which included itemssuch as work and activities and loss of motivated behav-ior), have been implicated in the performance of cogni-tive tasks in which attention needs to be directed awayfrom the affective charge associated with the task.199

These findings suggest that regions found to corre-late with one or another aspect of depression severity inour study are involved in the identification and ap-praisal of the emotional significance of the stimulus andin the production of affective states, including the auto-matic physiologic and somatomotor responses to the emo-tional content of stimuli.

On the other hand, there is a lack of correlation in thisstudy with regions such as the dorsal anterior and rostralanterior cingulate, which are thought to be associated withattention to subjective emotional states and experi-ences,200 and with regions such as the paracingulate gy-rus, which has been associated with representation of men-tal states of self201 and self-reflecting thoughts.202 We suggestthat this has to do with the fact that the HDRS is a clinician-rated instrument. Consequently, it is likely that the sever-ity of depression as scored by clinicians, who tend to givemore credence to objective or behavioral and neuroveg-etative signs of depression, is more likely to correlate withthe activities of structures that are involved in the percep-tion and regulation of unconscious and autonomic, physi-ologic, and somatomotor responses to the affective con-tents of stimuli. We hypothesize that a self-rated scale, suchas the Beck Depression Inventory, would be more likelyto correlate with structures such as the anterior cingulatethat are thought to be involved in the effortful regulationof affective states and reflective awareness of affect (thethird process suggested by Phillips et al122).

We find that different brain regions contribute to dis-crete psychopathologic components that compose theoverall syndrome of major depressive disorder. There isa correlation between the HDRS total score and the com-ponent scores. Therefore, although the brain regions thatcorrelate with different factors are largely different, it isnot surprising that there still is some overlap betweenthe factors in terms of involved brain regions. Global se-verity correlates with brain regions that overlap with mostof the factors.

The overall pattern is striking in that the positive cor-relations with aspects of depression severity are mostlysubcortical ventral, ventral prefrontal, and limbic struc-tures and the negative correlations are mostly or almostexclusively dorsal cortical; this overall pattern is consis-tent with the literature.94

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Ultimately, understanding the functional neuro-anatomy of major depressive disorder will depend on un-derstanding the unique association of specific regionalnetworks and neurotransmitter systems to specific symp-tom components rather than to an overall composite se-verity score. The widespread correlation of depressivesymptom clusters with rCMRglu reported herein is mostconsistent with abnormalities in the distributed neuro-modulatory role of monoaminergic neurotransmitter sys-tems. Correlating regional brain abnormalities in cat-echolaminergic, serotonergic, and dopaminergic synaptictransmission with symptom components of depressionand neuropsychologic deficits of depression may pro-vide further insights into the neurobiologic processes ofmajor depression.

Submitted for Publication: October 8, 2003; final revi-sion received September 13, 2004; accepted September29, 2004.Correspondence: Matthew S. Milak, MD, Departmentsof Psychiatry and Radiology, Columbia University, De-partment of Neuroscience, New York State PsychiatricInstitute, 1051 Riverside Dr, Mail Unit 42, New York, NY10032 (mm2354@columbia.edu).Funding/Support: This study was supported in part bygrants MH40695, MH62185, and RR00645 from the Na-tional Institutes of Health, Bethesda, Md, and by the Na-tional Alliance for Research on Schizophrenia and De-pression, Great Neck, NY.Previous Presentation: This study was presented in partat the Annual Meeting of the Organization for HumanBrain Mapping; June 19, 2003; New York, NY.Acknowledgment: We thank our imaging core for ana-lyzing the images, our clinical evaluation core for re-cruiting the patients and performing the clinical rat-ings, and Shuhua Li, PhD, for his assistance with thepolychoric statistics.

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